检索结果-南通市图书馆

uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1317-1330页

作者： Jiaxin Ren Jingcheng Wen Zhibin Zhao Ruqiang Yan Xuefeng Chen Asoke K.Nandi School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China Department of Electronic and Electrical Engineering Brunel University LondonKingston LaneUxbridgeUB83PHUK IEEE

Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art performance.However,the deep learning model is often not truly trusted by users due to the lack of interpretability of“black box”,which limits its deployment in safety-critical applications.A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases,and the human in the deci-sion-making loop can be found to deal with the abnormal situa-tion when the models fail.In this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks,called SAEU.In SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural networks.Based on the SAEU,we propose a unified uncertainty-aware deep learning framework(UU-DLF)to realize the grand vision of trustworthy fault diagnosis.Moreover,our UU-DLF effectively embodies the idea of“humans in the loop”,which not only allows for manual intervention in abnor-mal situations of diagnostic models,but also makes correspond-ing improvements on existing models based on traceability analy-sis.Finally,two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.

关键词： Out-of-distribution detection traceability analysis trustworthy fault diagnosis uncertainty quantification.

来源：

维普期刊数据库

同方期刊数据库博看期刊评论

在线全文

学校读者我要写书评

暂无评论

Efficient Collocational Approach for Parametric uncertainty Analysis

引用

Communications in Computational Physics 2007年第2期2卷 293-309页

作者： Dongbin Xiu Department of Mathematics Purdue UniversityWest LafayetteIN 47906USA.

A numerical algorithm for effective incorporation of parametric uncertainty into mathematical models is presented.The uncertain parameters are modeled as random variables,and the governing equations are treated as stochastic.The solutions,or quantities of interests,are expressed as convergent series of orthogonal polynomial expansions in terms of the input random parameters.A high-order stochastic collocation method is employed to solve the solution statistics,and more importantly,to reconstruct the polynomial expansion.While retaining the high accuracy by polynomial expansion,the resulting“pseudo-spectral”type algorithm is straightforward to implement as it requires only repetitive deterministic simulations.An estimate on error bounded is presented,along with numerical examples for problems with relatively complicated forms of governing equations.

关键词： Collocation methods pseudo-spectral methods stochastic inputs random differential equations uncertainty quantification.

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Galerkin Method for Wave Equations with Uncertain Coefficients

引用

Communications in Computational Physics 2008年第2期3卷 505-518页

作者： David Gottlieb Dongbin Xiu Division of Applied Mathematics Brown UniversityProvidenceRI 02912USA Department of Mathematics Purdue UniversityWest LafayetteIN 47907USA.

Polynomial chaos methods(and generalized polynomial chaos methods)have been extensively applied to analyze PDEs that contain uncertainties.However this approach is rarely applied to hyperbolic systems.In this paper we analyze the properties of the resulting deterministic system of equations obtained by stochastic Galerkin projection.We consider a simple model of a scalar wave equation with random wave speed.We show that when uncertainty causes the change of characteristic directions,the resulting deterministic system of equations is a symmetric hyperbolic system with both positive and negative eigenvalues.A consistent method of imposing the boundary conditions is proposed and its convergence is established.Numerical examples are presented to support the analysis.

关键词： Generalized polynomial chaos stochastic PDE Galerkin method hyperbolic equation uncertainty quantification.

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：